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Cover Article

Informing electrification strategies of residential neighborhoods with urban building energy modeling

Tianzhen Hong( )Sang Hoon LeeWanni ZhangHan LiKaiyu SunJoshua Kace
Building Technology and Urban Systems Division, Lawrence Berkeley National Laboratory, USA
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Abstract

Electrifying end uses is a key strategy to reducing GHG emissions in buildings. However, it may increase peak electricity demand that triggers the need to upgrade the existing power distribution system, leading to delays in electrification and needs of significant investment. There is also concern that building electrification may cause an increase of energy costs, leading to further energy burden for low-income communities. This study uses the urban scale building modeling tool CityBES to assess the electrification impacts of more than 43,000 residential buildings in a neighborhood of Portland, Oregon, USA. Energy efficiency upgrades were investigated on their potential to mitigate the increase of peak electricity demand and energy burden. Simulation results from the calibrated EnergyPlus models show that electrification with heat pumps for space heating and cooling as well as for domestic water heating can reduce CO2e emissions by 38%, but increase peak electricity demand by about 9% from the baseline building stock. Combining electrification measures and energy efficiency upgrades can reduce CO2e emissions by 48% while reducing peak electricity demand by 6% and saving the median household energy costs by 28%. City and utility decision makers should consider integrating energy efficiency upgrades with electrification measures as an effective residential building electrification strategy, which significantly reduces carbon emissions, caps or even decreases peak demand while reducing energy burden of residents.

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Building Simulation
Pages 3-18

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Cite this article:
Hong T, Lee SH, Zhang W, et al. Informing electrification strategies of residential neighborhoods with urban building energy modeling. Building Simulation, 2025, 18(1): 3-18. https://doi.org/10.1007/s12273-024-1214-6

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Received: 01 September 2024
Revised: 16 October 2024
Accepted: 31 October 2024
Published: 23 December 2024
© Lawrence Berkeley National Laboratory, under exclusive license to Tsinghua University Press 2024